1.Climate changes due to global warming 1
Climate risks comparison due Global Warming
Evaluation based on Data Visualization Framework 1.Understand the audience It is easy to read and interprete the information.Impact of the climate change due to global warming on various entities like weather,water availability,people,species,sea-level rise,oceans is clearly mentioned 2.Big-picture considerations The visualization coneys proper amount of data and it doesn’t overload the reader with too much of data. Clear members are mentioned for many entities like weather,species,water availability etc but they are missing for the entities like oceans,cost and food. 3.Color The visualization uses complementary colors so that it doesn’t look flamboyant. 4.Text formatting The text used for representing the data could have been minimized. Just putting the numbers with the help of some symbols for increase and decrease could have been sufficient instead of writing the sentences for each entity. 5.Patterns As the entities shown in the visualization are not closely related to each other, there is no pattern shown relating them. 6.Consistent Scales The scales used to show increase or decrease are consistent throughout the visualization 7.Visually appealing design The visalization seems visually appealing to the layman. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.
2.Vulnerability to climate change due to Carbon dioxide emission 2
Evaluation based on Data Visualization Framework 1.Understand the audience It is not easy to read and interprete the information for a common man.Prior knowledge of the dataset is required before interpreting the visualization correctly. 2.Big-picture considerations The name of the countries are put in the circular fashion hence it is quite difficult to read those names. But this approach might not cause a problem for an expert reader. 3.Color The visualization uses complementary colors so that it doesn’t look too fancy. 4.Text formatting The amount of text used for representing the data is sufficient. 5.Patterns Increasing or decreasing circular patterns for each nation effectively represents CO2 emitting nations and vulnerable nations. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman.But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.
3.Climate change due to greenhouse effect 3
The role of the greenhouse effect on the temperature change
Evaluation based on Data Visualization Framework
1.Understand the audience It is not easy to relate and interprete the information shown in the two graphs. 2.Big-picture considerations The data represented is too statistical and not easily readable. 3.Color The visualization uses decent colors to represent the two graphs. 4.Text formatting The amount of text used in the visualization is very limited. 5.Patterns Patterns are not applicable for this type of visualization using graphs. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman.But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.
4.Climate change versus % change in the population 4
Climate change vulnerability for the fastest growing african cities)
Evaluation based on Data Visualization Framework 1.Understand the audience It is not easy to understand the visualization.Prior knowledge of the dataset is necessary. 2.Big-picture considerations The definition of Climate change vulnerability index should be specified somewhere, else it is very difficult to interprete the information. 3.Color The visualization uses dark colors for each continent. The significance of the background color is not specified/easily understood. 4.Text formatting The amount of text used in the visualization is limited.
5.Patterns The circular patterns vary in sizes and it is difficult to understand the difference between the two circles with almost similar sizes. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman as information is cluttered throughout the visualization. But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.
5.Climate changes due to Sun’s energy 5
Climate change due to Sunâs energy
Evaluation based on Data Visualization Framework 1.Understand the audience It is not easy to understand the visualization.Prior knowledge of the dataset is necessary. 2.Big-picture considerations Too much information is represented in a small area.The use of bar charts would have been more useful as bar charts are easy to read. 3.Color The visualization uses decent colors to represent the two graphs. 4.Text formatting The amount of text used in the visualization is limited.
5.Patterns Patterns are not applicable for this type of visualization using graphs. 6.Consistent Scales The scales are consistent throughout the visualization 7.Visually appealing design The visalization doesn’t seems visually appealing to the layman as information is not easily readable. But it might be easily understood by an expert who is equipped with the prior knowledge of the data. 8.Graphical Integrity Graphical integrity is maintained throughout the visualization.
After completing the literature survey for effective evaluation of data visualizations, 'Data Visualization Framework has been designed. The framework consists of 8 key criteria which are very critical in evaluating the data visualizations. Out of the total five visualizations evaluated, none of the visualizations seems perfect matching all the eight criteria.The exercise of designing a data visualization framework is really helpful and led us to conduct deep research and meticulous analysis of the existing data visualizations.